Towards Generating Real-World Time Series Data

Abstract

Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usually with the assumption that the targeted time series data are well-formatted and complete. However, real-world time series (RTS) data are far away from this utopia, e.g., long sequences with variable lengths and informative missing data raise intractable challenges for designing powerful generation algorithms. In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges. RTSGAN first learns an encoder-decoder module which provides a mapping between a time series instance and a fixed-dimension latent vector and then learns a generation module to generate vectors in the same latent space. By combining the generator and the decoder, RTSGAN is able to generate RTS which respect the original feature distributions and the temporal dynamics. To generate time series with missing values, we further equip RTSGAN with an observation embedding layer and a decide-and-generate decoder to better utilize the informative missing patterns. Experiments on the four RTS datasets show that the proposed framework outperforms the previous generation methods in terms of synthetic data utility for downstream classification and prediction tasks.

Accepted in 21th IEEE International Conference on Data Mining (ICDM 2021). Code is available at https://seqml.github.io/rtsgan

References in corpus (7)

• C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
• Variational Recurrent Auto-Encoders
• Time2Vec: Learning a Vector Representation of Time
• COT-GAN: Generating Sequential Data via Causal Optimal Transport
• Multi-Time Attention Networks for Irregularly Sampled Time Series
• Forecasting in multivariate irregularly sampled time series with missing values